1. Field of the Invention
The invention pertains to a method of condition monitoring of a fleet of plants, machines, or processes. It pertains in particular to a method of condition monitoring of a fleet of electrical power generation plants as well as machines and processes associated with such power plants.
2. Brief Description of the Related Art
The operation of electrical power plants and associated machines and processes are frequently monitored in order to determine the condition of its individual components, values of operational parameters, and the performance of the plant as a whole. Condition monitoring allows optimization of plant operation and determination of expected operational lifetime of its components. It is also intended for early detection of operational parameters that exceed given normal operational ranges and alerting of plant engineers of operational problems or failure developments. As such, it can effectively reduce potential and costly downtime and increase operation safety and performance of a power plant or its associated machines. It also allows reliable indication of necessary maintenance.
Various methods of monitoring machines or plants have been presented in the state of the art. Many of these are directed to the monitoring of a single machine or process, such as for example in WO 2004/042531 and WO 2005/008420. These methods can typically be realized using standard computing tools within a reasonable time frame, and with reasonable computing power.
WO 02/086726 discloses a method of monitoring and controlling a single machine or process using a combination of diagnostics and model-based monitoring and control. Estimated sensor values are generated by an empirical model based system and compared to actual sensor values. Residual values obtained by subtraction of estimated values from real values are subjected to a sequential probability ratio test, which allows early detection of deviations of the residuals from a threshold value.
WO 02/057856 discloses a predictive monitoring of a single process or machine based on empirical model-based surveillance or control. For the monitoring method, a representative set of sensor data is used that is consistently adapted and updated by the addition of newly acquired values, which replace the previous values.
US 2004/0243636 discloses a method of monitoring the condition of an entire fleet of power plants. In this method a common software platform serves to monitor the fleet, where a modeling method is coupled with an incident logic engine for registering power plant incidents. A specific model is created for each member or asset of the fleet to be monitored. For this, historic data from each plant is used to create a model for an operating condition considered normal. Each specific model is called up individually to generate a model for that particular fleet member, which is used to estimate engine operation parameters during real-time operation. The estimated operational parameters are compared to actual measurements of the same parameters in order to produce residual signals, which can indicate the condition of the plant.
Each individual empirical model is adapted incrementally, requiring separate model maintenance for each member in the fleet. The maintenance of the models involves the analysis of a large amount of data for each member of the fleet and hence a large effort in terms of engineers' time and computing power.